US4644479A - Diagnostic apparatus - Google Patents

Diagnostic apparatus Download PDF

Info

Publication number
US4644479A
US4644479A US06/636,195 US63619584A US4644479A US 4644479 A US4644479 A US 4644479A US 63619584 A US63619584 A US 63619584A US 4644479 A US4644479 A US 4644479A
Authority
US
United States
Prior art keywords
sensor
sample
indications
operable
subsystem
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
US06/636,195
Other languages
English (en)
Inventor
Christian T. Kemper
James C. Bellows
Pamela J. Kleinosky
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hughes Tool Co
CBS Corp
Original Assignee
Westinghouse Electric Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Family has litigation
First worldwide family litigation filed litigation Critical https://patents.darts-ip.com/?family=24550860&utm_source=google_patent&utm_medium=platform_link&utm_campaign=public_patent_search&patent=US4644479(A) "Global patent litigation dataset” by Darts-ip is licensed under a Creative Commons Attribution 4.0 International License.
Application filed by Westinghouse Electric Corp filed Critical Westinghouse Electric Corp
Priority to US06/636,195 priority Critical patent/US4644479A/en
Assigned to WESTINGHOUSE ELECTRIC CORPORATION, A CORP. OF PA reassignment WESTINGHOUSE ELECTRIC CORPORATION, A CORP. OF PA ASSIGNMENT OF ASSIGNORS INTEREST. Assignors: BELLOWS, JAMES C., KEMPER, CHRISTIAN T., KLEINOSKY, PAMELA J.
Priority to CA000484801A priority patent/CA1237794A/en
Priority to IN502/CAL/85A priority patent/IN164364B/en
Priority to AU45143/85A priority patent/AU572674B2/en
Priority to MX206084A priority patent/MX157037A/es
Priority to JP60169646A priority patent/JPS6155304A/ja
Priority to DE8585305410T priority patent/DE3582625D1/de
Priority to EP85305410A priority patent/EP0170516B1/en
Priority to ES545729A priority patent/ES8700743A1/es
Priority to KR1019850005569A priority patent/KR860000851A/ko
Publication of US4644479A publication Critical patent/US4644479A/en
Application granted granted Critical
Assigned to HUGHES TOOL COMPANY reassignment HUGHES TOOL COMPANY CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). EFFECTIVE OCTOBER 11, 1988 (DELAWARE) Assignors: HUGHES TOOL COMPANY-USA, A CORP. OF DE
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/911—Nonmedical diagnostics
    • Y10S706/914—Process plant
    • Y10S706/915—Power plant

Definitions

  • the invention in general relates to diagnostic apparatus, and particularly to a sensor-based system for on-line realtime monitoring.
  • Complex industrial or other operating systems generally have a plurality of sensors for monitoring various parameters during operation, not only for control purposes but for purposes of monitoring the system to detect actual or impending malfunctions.
  • Some systems may utilize dozens, if not hundreds, of sensors in the diagnostic process and very often the sensors may fail, degrade, or provide spurious readings not related to the actual parameter being measured.
  • a malfunction may be indicated where, in fact, no malfunction exists and conversely a malfunction may be occurring or may occur without its detection and without proper notification to the system operator. Such event can represent a tremendous economic loss as well as a potentially dangerous situation.
  • some systems utilize redundant sensors such that if one fails another may take its place. For systems utilizing hundreds of sensors, such solution may be unfeasible, from an economic standpoint.
  • sensor readings are preprocessed prior to the system diagnosis and eliminated from consideration if their readings exceed certain boundaries. With such an arrangement, however, valuable information relative to the sensor degradation history which may be utilized in the diagnostic process, is lost.
  • the present invention provides for a diagnostic system which can recognize operating problems while they may be little more than a vague trend, and may do so taking into account sensor degradation or failure.
  • Diagnostic apparatus for monitoring an operating system having a plurality of sensors throughout the system and which generate output signals indicative of certain system parameters.
  • a control establishes a first subsystem for each of a number of selected sensors and includes means to periodically obtain readings of the sensor output signals to provide a plurality of indications characterizing movement, if any, of the sensor signal.
  • Such movement indication may include whether or not a sensor signal has sharply increased or decreased in a first period of time, whether it has slowly increased or decreased in a second and greater period of time or whether or not the signal is steady during the time periods.
  • the control is additionally operable to establish a second subsystem which is responsive to the sensor readings as well as selected ones of the movement indications to provide validated conclusions relative to the sensor signal with each conclusion having a certain confidence factor in the validity of the conclusion.
  • the control also establishes a third subsystem which is responsive to the sensor signals as well as the validated conclusions obtained by the second subsystem to provide indications of possible malfunctions in the operating system whereby such malfunctions, with a certain degree of confidence, may be communicated to an operator.
  • diagnostic apparatus in accordance with the present invention is illustrated with respect to a steam turbine-generator power plant which utilizes a plurality of sensors for obtaining data relative to chemical parameters at the influent and effluent of chemically active components to predict possible malfunctions of these components.
  • FIG. 1 illustrates a simplified diagram of a steam turbine generator power plant
  • FIGS. 2 and 3 illustrate nodal diagrams utilized to explain one type of expert system which may be utilized in the operation of the diagnostic apparatus
  • FIGS. 4A and 4B illustrate various functions associated with components of FIGS. 2 and 3;
  • FIG. 5 is a nodal diagram subsystem illustrating the propagation of belief relative to certain parameters of any sensor utilized in the arrangement of FIG. 1;
  • FIG. 6 is a nodal diagram subsystem illustrating the propagation of belief of certain characteristics relative to a particular sensor of the arrangement of FIG. 1;
  • FIG. 7 is a simplified diagram of the type of sensor used in FIG. 6;
  • FIG. 8 is a nodal diagram subsystem illustrating the propagation of belief of certain characteristics relative to another particular sensor of the arrangement of FIG. 1;
  • FIG. 9 is a simplified diagram of the type of sensor used in FIG. 8;
  • FIG. 10 is a nodal diagram subsystem illustrating the propagation of belief relative to a certain malfunction of a component of FIG. 1;
  • FIG. 11 illustrates one type of display for presentation of possible malfunctions.
  • the plant includes a steam turbine arrangement 10 having a plurality of turbines in the form of a high-pressure turbine 12, intermediate pressure turbine 14 and low pressure turbine 16, all of which are coupled to a common shaft 18 to drive an electrical generator 20 which supplies power to a load 22 when on-line with main circuit breakers 23 closed.
  • a steam supply such as a fossil-fired once-through boiler system 24 includes, by way of example, and input economizer section 26, a superheater 27 and a reheater 28.
  • Boiler steam is provided to the turbine arrangement 10 through input valving 30 and steam exiting the high-pressure turbine 12 is reheated in reheater 28 and provided to intermediate pressure turbine 14 through valving 32.
  • Steam exiting the intermediate pressure turbine 14 is provided by way of cross-over piping 34 to the low-pressure turbine 16 from which the steam is exhausted into a conventional condenser 36 in heat exchange relationship with externally supplied cooling water.
  • the chemical treatment may include a plurality of condensate polishers 40 which basically are ion exchange units designed to remove certain impurities.
  • the water is heated by a series of heaters 42 including a deaerating heater which removes dissolved gases, and, after addition of certain chemicals, is returned to the input economizer 26 of the boiler system 24.
  • the power plant is provided with a plurality of sensors including sensors to monitor chemical parameters of the steam in the turbine system as well as condensate in the path between the condenser and boiler system.
  • sensor array 50 is provided and includes a plurality of sensors S1, S2 . . . Sn which receive sample steam from the steam path and reduced to suitable pressure and temperature by conditioner 52.
  • a plurality of other sensor arrays 54 to 56 are respectively provided at the output of condenser 36, after the polishers 40 and prior to economizer 26.
  • the sensors of each array may include those for measuring acid cation exchanged conductivity, sodium concentration, dissolved oxygen, specific conductivity, pH and chloride concentration.
  • the sensor arrays are positioned at the influent and effluent of chemically active components of the steam turbine system.
  • sensor array 50 in the steam path measures the influent to condenser 36 while sensor array 54 measures the effluent.
  • Sensor array 54 in turn provides data relative to the influent to condensate polishers 40 while sensor array 55 measures the effluent therefrom.
  • Data relative to the influent and effluent of chemical feeds, and heaters 42 are provided by respective sensor arrays 55 and 56 while sensor arrays 56 and 50 provide information relative to the influent and effluent of boiler system 24.
  • the sensors can provide not only indications of corrosive and other impurities in the system but are strategically located to provide indications of plant component malfunctions as well.
  • the malfunction assessments are provided by a digital computer 58 controlling the diagnostic process so as to provide possible malfunction indications which may be communicated to an operator via an output 60 such as an alarm system, CRT or other display, message system or combinations thereof.
  • the computer controls the diagnostic process by implementation of an expert system computer program that uses knowledge representations and inference procedures to reach conclusions normally determined by a human expert.
  • a common form of knowledge representation is in the form of IF . . . THEN rules and one such system which may be utilized in the practice of the present invention is PDS (Process Diagnosis System) described in the Proceedings of the Eight International Joint Conference on Artificial Intelligence, Aug. 8-12 1983, pages 158-163.
  • PDS Process Diagnosis System
  • evidence 64 is linked to the consequent hypothesis 65 by means of rule 66, with the evidence and hypothesis constituting nodes of the system.
  • Numeral 68 represents a supporting rule of node 64, that is, a rule for which the node 64 is a hypothesis.
  • Rule 66 is a supported rule of node 64, that is, a rule for which the node 64 is evidence.
  • rule 66 is a supporting rule for node 65.
  • nodes can take the form of evidence, hypothesis, malfunctions, sensors and storage-nodes which are nodes capable of storing values input from other nodes and performing some predetermined mathematical operation on the values.
  • MB a measure of belief
  • MD a measure of disbelief
  • An expert in the field to which the diagnosis pertains establishes the various rules and relationships which are stored in the computer's memory and utilized in the diagnostic process.
  • the expert's belief in the sufficiency of the rule is also utilized. This belief, which represents the experts opinion as to how the presence of evidence proves the hypothesis, is given a numerical representation designated as a sufficiency factor, SF, which ranges from -1 to +1, where positive values of SF denote that the presence of the evidence suggests that the hypothesis is true and negative values denote that the presence of the evidence suggests that the hypothesis is not true.
  • PDS additionally utilizes the expert's belief in the necessity of the rule, which illustrates to what degree the presence of the evidence is necessary for the hypothesis to be true.
  • This necessity belief is given a numeral representation designated as a necessity factor NF which ranges from -1 to +1 where positive values of NF denote that the absence of evidence suggests that the hypothesis is not true and negative values denote that the absense of the evidence suggests that the hypothesis is true.
  • FIG. 3 illustrates another common arrangement wherein a plurality of rules 68 to 70 connect evidence nodes 72 to 75 to a hypothesis node 76.
  • Element 78 represents the combining of evidence in (a) a disjunctive manner, that is, if evidence 74 OR 75 is present, or (b) in a conjunctive manner, that is, if evidence 74 AND 75 are present.
  • Belief leading to a consequent possible malfunction in the system being diagnosed is propagated from evidence to hypothesis in repetitive cycles, at the beginning of which the CF, MB and MD values of each node are reset to zero (except for a sensor node where the MB and accordingly the CF is assumed to be +1).
  • the evidence CF is positive and the SF is positive, then the MB of the hypothesis is increased; if the SF is negative, then the MD of the hypothesis is increased.
  • final values are obtained by examining each rule in sequence and performing the calculations for each rule in accordance with the following, where MB old and MD old are the belief and disbelief in the rule's hypothesis before each calculation, CF the confidence in the rule's evidence, SF and NF are the rule's sufficiency and necessity and MB new and MD new are the belief and disbelief in the rule's hypothesis after each calculation:
  • a rule's sufficiency (SF) or necessity (NF) may in many instances by expressed as a constant. In other instances, the sufficiency and/or necessity may be expressed as some other function which will generate a sufficiency or necessity factor of a fixed number by evaluating the function for a particular variable.
  • a common function which may be utilized is a piece-wise linear function, two examples of which are illustrated in FIGS. 4A and 4B.
  • the Y-axis in these figures represent the SF (or NF) ranging from -1 to +1 on the vertical scale.
  • the X-axis horizontal scale represents the value of some variable such as a sensor reading or the result of some mathematical operation, by way of example.
  • FIG. 4A if the variable has a value between 0 and a, or is greater than f, it will generate an SF of -1 whereas if the value is between c and d, it will generate an SF of +1. Values between a and c or d and f will generate corresponding SFs between -1 and +1.
  • FIG. 4B represents a piece-wise linear function wherein any variable value greater than b will generate an SF of +1, any variable value less than -b will generate an SF of -1 and values between -b and +b will generate a corresponding SF between -1 and +1.
  • Another type of useful rule is a reading-transform rule which, when carried out, applies a transform function to the value found in the rule's evidence node. If the evidence node is a sensor, the value is a sensor reading, with appropriate conversion, scaling, etc. performed by the transform, if needed.
  • the various types of sensors utilized to gather data are themselves subject to erroneous operation and therefore may lead to an erroneous diagnosis of plant components.
  • the computer controlling the diagnosis operation establishes a first subsystem which is generic to all sensors and which may be instantiated, or applied, with respect to selected sensors throughout the system.
  • a typical generic subsystem utilizing the expert system nodal and rule concept is illustrated in FIG. 5.
  • the arrangement is responsive to the sensor output signals to provide an indication of sensor signal movement.
  • an item designated as a sensor is a node into which actual sensor readings are placed.
  • Sensor 100 periodically provides sensor readings which are linked by a reading transform rule 102 to node 104 which is operable to obtain an average of the last five sensor readings so as to provide some stability and filter out any sensor noise.
  • reading transform rule 106 links the sensor readings to node 108 which is operable to obtain the value of the current sensor reading minus the previous sensor reading so as to be sensitive to sensor variation and provide an indication of just how much noise is present in the system.
  • Baseline function 110 linked to the sensor by reading transform rule 112 is operable to provide a relatively longer term indication of sensor performance. Let it be assumed, merely by way of example, that sensor readings are periodically provided once a minute. Baseline function 110 may be operable to accumulate readings over a period of time such as 30 minutes and provide the average of, for example, the first 10 readings of the 30 minute period.
  • function 108 provides indications of changes from minute to minute
  • function 104 provides a 5 minute average
  • baseline function provides an average over an interval in past time.
  • Node 114 determines the change, if any, between the current sensor reading provided by rule 116 and the average of the last 5 provided by rule 118.
  • the amount of change relative to the average is determined at node 120 which functions to divide the value of change by the average of the last five readings. To accomplish this, the relative change node 120 is linked by rule 122 to node 114 and by rule 124 to node 104.
  • Trending information is obtained by node 126 which is linked to the last five average node 104 by rule 128 and to baseline node 110 by rule 130. Functionally, the slope is obtained by subtracting the baseline value from the last five average and dividing by the time period between the centers of the data group. That is, the time period between the center of the ten readings taken 30 to 20 minutes ago and the center of the last five readings is 22.5 time units or 221/2 minutes in the present example.
  • the relative slope is obtained at node 132 by dividing the slope, linked by rule 134, by the baseline, linked by rule 136.
  • belief in a positive relative slope at node 132 is propagated to node 146 via rule 148 and belief in a negative relative slope is propagated to nodeU 150 via rule 152.
  • nodes 138 and 142 set forth information with a certain degree of confidence with respect to relatively short term sensor changes.
  • Nodes 146 and 150 set forth information with a certain degree of confidence with respect to relatively longer term changes in sensor readings. Confidence in these beliefs, however, will be lowered if the sensor readings are close to the detection limit of the sensor itself in which case, certain values may become unstable militating against drawing large conclusions based on these values.
  • the subsystem includes means for adding disbelief to these nodes via rules 154 to 157 should be sensor be operating near its detection limit.
  • Node 160 determines the ratio of the average sensor reading compared to the detection limit, with the average being propagated by rule 162 from node 104 and the detection limit being provided by a detection limit node 164 via rule 166. If the ratio calculated at node 160 is less than one, for example, belief is propagated to node 168 via rule 170 that the apparatus is in fact near the detection limit so as to modify belief in the short term and long term changes.
  • the short term nodes 172 and 176 and the long term nodes 180 and 184 are evidence for whether or not this sensor output is steady as defined by node 188 and connected to these previous nodes by respective rules 189 to 192. All of these latter rules would have negative sufficiency functions creating disbelief in the steady hypothesis, since, if the sensor output is rising or dropping or increasing or decreasing, it is not steady.
  • a positive belief in a steady situation may be propagated from node 194 connected to steady node 188 by rule 196 and indicative of a small variation. That is, if the sensor output signal does not change from reading to reading, or changes by a very small predetermined amount, then this situation is indicative of a steady condition.
  • the change from reading to reading as developed at node 108 is propagated via rule 200, which takes the absolute value of the change, and node 198 derives the value for average change.
  • a piece-wise linear function associated with rule 202 would then propagate belief or disbelief to node 194 depending upon the value of the average change.
  • the subsystem of FIG. 5 includes means for determining whether or not a scale change has been made. This is accomplished with the provision of node 204 which determines the ratio of the average of the last five readings from node 104 via rule 206 to the baseline value from node 110 via rule 208. Depending upon the value derived at node 204, a piece-wise linear function associated with rule 210 would propagate the belief that there has or has not been a scale change.
  • the sensor instrument includes means for changing the scale by factors of ten.
  • the piece-wise linear function associated with rule 210 therefore would be +1 for an average to baseline ratio of around 0.1, 1, 10, 100, etc., and -1 for other ratios.
  • the subsystem of FIG. 5 derives a great deal of information relative to the operation of any sensor to which the subsystem is applied and this information, reproduced at the bottom of FIG. 5, is utilized in subsequent subsystems established by the computer control.
  • FIG. 6 pertains, by way of example, to the obtaining of validated readings from a cation conductivity sensor.
  • the subsystem of FIG. 6 is generic to any cation conductivity sensor which may be utilized in the arrangement of FIG. 1 be it for steam measurement or condensate measurement.
  • a cation conductivity sensor is utilized to exchange cations in a sample to hydrogen so that anions, commonly associated with corrosion, can be measured.
  • FIG. 7 illustrates a simplified cation conductivity sensor.
  • the sensor includes a cation resin bed 222 which removes the cations present in the sample and replaces them with hydrogen ions.
  • any sodium chloride present in the sample is converted to hydrochloric acid, sodium sulfate is converted to sulfuric acid, sodium acetate is converted to acetic acid, etc.
  • a conductivity cell 224 then provides an output signal proportional to the conductivity of the cation exchanged sample to provide an indication of anion concentration.
  • the subsystem includes several nodes for characterizing the sensor output signal.
  • a determination of whether or not the sensor reading is low is made at node 230 connected to the sensor by rule 232 and the determination of whether or not the sensor reading is high is accomplished at node 234 connected to the sensor by rule 236.
  • Most sensors have provisions for testing the sensor electronics and under such test condition, any output signal provided is in an abnormally high range. Node 238 tests to see whether or not the sensor is in this abnormally high range and is connected to the sensor by rule 240.
  • the sensor reading is low, it may be an indication that someone has turned the sensor off (many sensors will still provide a small output when turned off). Belief in the sensor being off may be generated at node 242 linked to node 230 by rule 244. If, however, the sensor reading is not steady, it means that the output readings are changing which strongly suggests that the sensor would not be in an off condition. Node 188 linked to the sensor off node 242 by rule 246 provides this indication of whether or not the sensor reading is steady. This indication is obtained from the steady indication of the subsystem of FIG. 5 as now applied to the specific cation conductivity sensor 220 of FIG. 6. In FIG. 6, as well as in subsequent Figs., nodes utilized from other subsystems are illustrated as dotted rectangles.
  • a further indication of the sensor being in an off condition is whether or not the sensor reading is equal to the system average as determined at node 248 linked by rule 250 to node 242.
  • a node 252 provides an indication of the average readings of all cation conductivity sensors in the system of FIG. 1 and links it by rule 254 to node 256 which is linked to this one particular sensor's output by rule 258 in order to derive an indication of the difference between this sensor's reading and the system average.
  • the result is linked to node 248 by rule 260 which would have a positive sufficiency factor if the results of the subtraction process of node 256 were within a predetermined range of zero.
  • Another factor which may yield a low sensor reading would be a contaminated or dirty sensor.
  • the conductivity electrodes may get contaminated such as by oil which then acts as an insulator resulting in an output reading which is not as high as it should be. This may be determined by the dirty sensor node 262 linked to the low sensor reading node 230 by rule 264.
  • a steady indication linked by rule 266 from node 188 may add to the confirmation of a dirty sensor, however, if the reading is not steady, it does not mean that the sensor isn't dirty. Confidence may be lost in a dirty sensor diagnosis if the sensor reading is equal to the system average with the dirty sensor node 262 being linked to this prior node by rule 268.
  • an OR function 280 is applied to nodes 172 and 176 indicative of a sensor reading rise or drop to determine whether or not there has been a recent sharp sensor reading change as determined at node 282 linked to the OR function by rule 284. With knowledge of a recent sharp sensor reading change propagated by rule 286 and belief as to whether or not a factor of 10 scale change is present propagated from node 212 by rule 288, node 290 may then make the appropriate scale change determination.
  • rule 292 propagates the belief in a high sensor reading from node 234 to node 294 for the determination of resin exhaustion. To substantiate that the sensor reading is high, it is compared with the system average. This step was previously done at node 256 with the belief in a higher reading being propagated by rule 296 to node 298 establishing the belief that the sensor reading is in fact higher. This belief, propagated by rule 300 adds to the belief that the sensor resin is probably exhausted.
  • the other input in the determination is via rule 304 from node 306 which establishes that the sensor reading is less than the specific conductivity of the sample.
  • the specific conductivity value is provided by a specific conductivity sensor 310 included as one of the plurality of sensors in the sensor array.
  • This specific conductivity value propagated by rule 312 and the cation conductivity sensor reading propagated by rule 314 are compared at node 316 wherein the specific conductivity value is subtracted from the sensor value with the result being propagated via rule 318 to node 306. If the resin is exhausted, then the value of the cation conductivity sensor output can get no higher than the specific conductivity sensor output. If there is evidence that the cation conductivity sensor value is not less than or equal to the specific conductivity value, then a measure of disbelief will be added to the determination of sensor resin exhaustion at node 294.
  • each malfunction has two or more inputs each lending a certain degree of concern relative to the existence of the malfunction such that the presence of the malfunction can be established with a certain confidence factor ranging from -1 (definitely not present) to +1 (definitely present).
  • the highest confidence in any of the possible malfunctions may be propagated by OR function 320 and rule 321 to node 322 indicative of a sensor malfunction. Since the sensor conclusions may be utilized in other subsystems to determine plant component malfunctions, the use of results from a failing or failed sensor would impact on accurate plant component malfunction predictions. For example, if there is a high sensor reading as determined at node 234, a logical conclusion is that there is a high anion concentration as specified at node 324 linked to the high sensor reading node 234 by rule 326. From an increasing reading determination made by the subsystem of FIG. 5, results propagated by rule 328 raises the belief in an increasing anion concentration specified by node 330.
  • paralt rule 350 will modify the sufficiency function of rule 332 to a degree depending upon the confidence in the sensor malfunction such that the confidence in the validated high anion concentration of node 334 is based on the sufficiency function of the modified rule.
  • paralt rule 352 will modify the sufficiency function of rule 336 so that confidence in a validated increasing anion concentration of node 338 is based upon the modified values of the rule.
  • the necessity functions are changed in a similar manner.
  • FIG. 8 illustrates a generic subsystem for validating the results of another type of sensor, namely a sodium sensor 356.
  • the subsystem of FIG. 8 may be instantiated with respect to any sodium sensor or any array throughout the power plant be it connected to a steam line or a liquid line.
  • a simplified diagram of a typical commercially available sodium sensor is illustrated in FIG. 9.
  • a sodium ladened input sample may be directed to a sensor path 358 or a polisher path 360 by means of a valve 362.
  • the sample is directed through tubing 364 passing through an injection port 366.
  • Tubing 364 is immersed in a concentrated ammonia environment 368 so that ammonia diffuses through tubing 364 at a predetermined rate to accurately control the pH value of the sample.
  • the sample is then provided to a set of electrodes 370 including a sodium ion selective electrode and a reference electrode to derive an output signal, the value of which represents parts per billion of sodium concentration.
  • polisher 372 removes almost all traces of sodium in the fluid supplied to tubing 364 and the output signal would then reflect a lack of or an extremely low sodium concentration. Calibration is then accomplished by injecting, at port 366, a known concentration of sodium as represented by arrow 374.
  • the subsystem obtains validated high and/or increasing sodium concentration indications as a function of the operating condition of the sensor.
  • High and low sensor reading indications in terms of sodium concentration are obtained at respective nodes 380 and 382 connected by respective rules 384 and 386 to sensor 356.
  • the low sodium concentration indication is utilized in conjunction with long term trending information to determine the condition of the sodium sensor itself.
  • time is propagated in the subsystem of FIG. 8 from a time sensor node 390.
  • current time is propagated via rule 392 to be continuously updated by node 394 connected to the drop indication by a paralt rule 396. If a drop does occur, another paralt rule 398 cuts off propagation of time via rule 392 such that node 394 stores the time, T' 0 , at which the sensor reading drop occurred.
  • Node 400 receives time T 0 propagated by rule 402 and the current time propagated by rule 404 and functions to continuously obtain the duration of time since the drop occurred.
  • Paralt rule 396 which fires when there is a sensor reading drop, functions to keep node 394 in an updated state, in the absence of which rule 402 would not fire to propagate time T 0 to node 400.
  • paralt rule 406 cuts off transmission of time via rule 408 to node 410 which, made valid by paralt rule 412, stores the intial time T' 0 at which the steady indication occurred. This time, propagated by rule 414 is subtracted from the current time propagated by rule 416, in node 418 to obtain an indication of the duration of the steady reading.
  • node 420 receives the duration of steady reading from node 418 via rule 422, the time since the sensor reading drop from node 400 via rule 424, and the low sensor reading from node 382 via rule 426.
  • a further input in the determination is communicated via rule 430 from node 432 which derives an indication of whether or not the particular sensor reading is equal to the system average. This indication is obtained in a manner similar to that of FIG. 6 by providing an average of all the sodium sensor outputs in the system, from node 434 to node 436 via rule 438.
  • the sensor reading itself is communicated via rule 440 to the node which then subtracts the system average from the sensor reading and provides the results to node 432 via rule 442. If, in fact, the sensor reading is equal to the system average, then this would contribute disbelief in the idea that the sensor was on the polisher path.
  • the arrangement of FIG. 8 also provides relatively long term trending information.
  • the average of the last five readings is communicated via rule 444 to node 446 which stores the values over a 4-hour period, for example, and takes the oldest average from 4 hours ago and subtracts it from the latest average to obtain the change over the 4 hours.
  • the relative 4 hour trend is obtained at node 448 connected to the 4 hour trend node 446 by rule 450 and connected to the average of the last 5 node 105 by rule 452.
  • Information relative to the trend propagated by rule 454 and the relative trend propagated by rule 456 will allow node 458 to determine whether or not the trend is positive. If the drive is positive and steady (i.e.
  • Node 460 connected to node 418 by rule 462 determines from the information of mode 418 whether or not there has been 4 hours of steady sensor reading. This fact, together with the positive trend are coupled to AND function 464 such that when both are present, rule 466 will propagate the belief to node 468 that there has been a rupture in the tubing. This belief is tempered, however, by disbelief propagated via rule 470 indicative of the sensor reading being equal to the system average.
  • OR function 472 takes the highest confidence of a possible tubing rupture or sensor being on the polisher path and propagates it via rule 474 to node 476 for determination of whether or not a sensor malfunction has actually occurred.
  • rule 480 propagates the belief in a high sodium concentration to node 482 which establishes a validated belief.
  • an increasing sensor reading from node 180 is propagated by rule 484 to node 486 providing an indication of validated increasing sodium concentration.
  • paralt rule 488 will modify the sufficiency and necessity functions of rule 480 to change the belief in a validated high sodium concentration.
  • paralt rule 490 will modify the sufficiency and necessity functions of rule 484 to change the belief in a validated increasing sodium concentration.
  • the computer control system establishes a third type of subsystem which utilizes the validated sensor indications to come to some valid conclusion relative to components of the operating system being diagnosed.
  • validated sensor readings are obtained at the influent and effluent of a particular component to make the malfunction diagnosis.
  • FIG. 10 illustrates a third type of subsystem which can diagnose whether or not condenser 36 has a leak, the consequence of which would be to allow cooling water to mix with the condensate contaminating it.
  • validated anion and sodium concentrations of the influent to condenser 36 are obtained by sensors of array 50 (FIG. 1) connected to the steam line and validated anion and sodium concentrations of the effluent of condenser 36 are obtained by sensors of array 54 connected to the condensate line.
  • sensors 496 and 497 are respective cation conductivity and sodium sensors connected to the steam path while sensors 498 and 499 are respective cation conductivity and sodium sensors connected in the condensate path. Since the steam turbine is chemically inactive, steam and condensate sensor readings should be identical in the absence of any condenser leak. With respect to the cation conductivity reading, node 500 receives respective condensate and steam sensor values via rules 501 and 502 to compute the difference therebetween.
  • the belief is propagated via rule 504 to node 506 which establishes that the anion concentration in the condensate is greater than the anion concentration in the steam, a condition which may indicate a leaky condenser since the anion concentrations should be equal.
  • Sodium sensor readings are propagated by rules 508 and 509 to node 510 to obtain a difference in the sodium concentrations in the condensate and steam. Since the sodium sensor provides such a wide dynamic range, the relative difference is obtained at node 512 to make sure that the difference is significant. The ratio is obtained by dividing the difference propagated via rule 514 by the actual sensor reading propagated by rule 516. If the relative difference is significant, propagation to node 518 via rule 520 results in the determination that the sodium in the condensate is greater than the sodium in steam, thereby lending to the concern that there is a condenser leak since the two readings should be similar.
  • Another type of sensor which may be utilized in the plant of FIG. 1 provides an indication of turbine load.
  • the subsystem of FIG. 5 as applied to the load sensor would reveal information including that illustrated at node 530 indicative of a decreasing load. If the condenser is leaking, the leak would be at a constant rate. With less load, there is less steam being condensed and the dilution of the leak is reduced such that the anion concentration in the condensate will increase.
  • AND function 532 is responsive to a decreasing load as well as a validated increasing anion concentration to propagate, via rule 534 the belief that the anion concentration is increasing with decreasing load, as specified at node 536.
  • condenser leak malfunction node 546 individually do no more than create concern, some more than others. Conversely, the absence of such conditions influence the negative belief in a condenser leak. Collectively, however, strong evidence of the condenser leak malfunction (or lack thereof) may be obtained and presented on display 60.
  • diagnostic apparatus which utilizes, in a preferred embodiment, an expert system to establish a first type of subsystem which obtains certain information relative to the output of an associated sensor. This information may then subsequently be used in a second type of subsystem which obtains an indication as to the validity of that sensor's output conclusions.
  • a third type of subsystem utilizes validated sensor indications to obtain malfunction indications of the system being diagnosed.
  • valid data relative to the influent and effluent of the condenser was utilized to diagnose a possible condenser leak.
  • the same principles would apply to other components of the system such as the determination of condensate polisher exhaustion from polishers 40 of FIG. 1 by obtaining validated data relative to its influent and effluent by sensors of arrays 54 and 55.
  • the establishment of these subsystems allows simultaneous determination of these component malfunctions as well as the determination of malfunctions in the sensor themselves.
  • FIG. 1 illustrates the sensor data being directly connected to the diagnostic computer 58, it is to be understood that such data at the plant could initially be collected and stored at the plant for subsequent transmission to a remote location where the diagnosis would be performed, such as described and claimed in copending application Ser. No. 605,703, filed Apr. 30, 1984, now U.S. Pat. No. 4,517,468.
  • FIG. 11 illustrates one of many different displays which may be utilized to convey malfunction information to a system operator.
  • the left-hand side of display 60 lists all possible malfunctions M1 to Mn. These malfunctions would be spelled out in an actual display. Confidence in the malfunction is displayed as a horizontal bar which can occupy the scale between -1 and +1 on the display.
  • any bar ranging from the zero position (the vertical line) in a negative direction or in a positive direction up to a distance of a (see scale at top of the figure) may be displayed as the color green indicating little or no concern.
  • a confidence factor calculated to have a value greater than a but less than b may be displayed in a second color such as yellow, signifying a situation of some concern. Confidence factors calculated to have a value greater than b may be displayed in a third color such as red, indicative of great concern.
  • malfunctions 1, 2, and n would be of no concern to the operator, malfunction M3 represents a condition which should be watched, and malfunction M4 a condition for which action may need to be taken.
  • distance a may correspond to a confidence factor of 0.3 and distance b may correspond to a confidence factor of 0.5.

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Veterinary Medicine (AREA)
  • Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Control Of Turbines (AREA)
  • Monitoring And Testing Of Nuclear Reactors (AREA)
  • Vehicle Body Suspensions (AREA)
  • Air Bags (AREA)
  • Electrical Discharge Machining, Electrochemical Machining, And Combined Machining (AREA)
US06/636,195 1984-07-31 1984-07-31 Diagnostic apparatus Expired - Fee Related US4644479A (en)

Priority Applications (10)

Application Number Priority Date Filing Date Title
US06/636,195 US4644479A (en) 1984-07-31 1984-07-31 Diagnostic apparatus
CA000484801A CA1237794A (en) 1984-07-31 1985-06-21 Diagnostic apparatus
IN502/CAL/85A IN164364B (ja) 1984-07-31 1985-07-05
AU45143/85A AU572674B2 (en) 1984-07-31 1985-07-18 Diagnostic apparatus for sensor-based system
MX206084A MX157037A (es) 1984-07-31 1985-07-24 Mejoras en aparato para diagnostico para vigilar un sistema de operacion sujeto a funcionamiento erroneos
ES545729A ES8700743A1 (es) 1984-07-31 1985-07-30 Un aparato de diagnostico para vigilar un sistema funcional sujeto a funcionamientos erroneos
JP60169646A JPS6155304A (ja) 1984-07-31 1985-07-30 診断装置
DE8585305410T DE3582625D1 (de) 1984-07-31 1985-07-30 Diagnostikgeraet.
EP85305410A EP0170516B1 (en) 1984-07-31 1985-07-30 Diagnostic apparatus
KR1019850005569A KR860000851A (ko) 1984-07-31 1985-07-31 진 단 장 치

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US06/636,195 US4644479A (en) 1984-07-31 1984-07-31 Diagnostic apparatus

Publications (1)

Publication Number Publication Date
US4644479A true US4644479A (en) 1987-02-17

Family

ID=24550860

Family Applications (1)

Application Number Title Priority Date Filing Date
US06/636,195 Expired - Fee Related US4644479A (en) 1984-07-31 1984-07-31 Diagnostic apparatus

Country Status (10)

Country Link
US (1) US4644479A (ja)
EP (1) EP0170516B1 (ja)
JP (1) JPS6155304A (ja)
KR (1) KR860000851A (ja)
AU (1) AU572674B2 (ja)
CA (1) CA1237794A (ja)
DE (1) DE3582625D1 (ja)
ES (1) ES8700743A1 (ja)
IN (1) IN164364B (ja)
MX (1) MX157037A (ja)

Cited By (155)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4754410A (en) * 1986-02-06 1988-06-28 Westinghouse Electric Corp. Automated rule based process control method with feedback and apparatus therefor
US4754409A (en) * 1985-06-26 1988-06-28 International Business Machines Corporation Method for dynamically collecting current data from specified external processes and procedures for use in an expert system
US4763277A (en) * 1986-01-17 1988-08-09 International Business Machines Corporation Method for obtaining information in an expert system
US4766550A (en) * 1985-10-30 1988-08-23 Westinghouse Electric Corp. Automatic on-line chemistry monitoring system
US4783752A (en) * 1986-03-06 1988-11-08 Teknowledge, Inc. Knowledge based processor for application programs using conventional data processing capabilities
US4792911A (en) * 1986-01-17 1988-12-20 Westinghouse Electric Corp. Diagnostic apparatus for an electric generator seal oil system
US4825195A (en) * 1986-05-07 1989-04-25 Yves Berruyer Method of monitoring to anticipate the triggering of an alarm
US4839822A (en) * 1987-08-13 1989-06-13 501 Synthes (U.S.A.) Computer system and method for suggesting treatments for physical trauma
US4839823A (en) * 1985-03-20 1989-06-13 Kabushiki Kaisha Toshiba Automatic trouble analysis apparatus and method thereof
US4841456A (en) * 1986-09-09 1989-06-20 The Boeing Company Test system and method using artificial intelligence control
US4885705A (en) * 1988-02-25 1989-12-05 Eastman Kodak Company Expert system shell for building photofinishing diagnostic systems
EP0347645A2 (en) * 1988-06-22 1989-12-27 Westinghouse Electric Corporation Monitoring a plurality of parameters from identical processes
US4901229A (en) * 1985-01-21 1990-02-13 Tsutomu Tashiro Parallelized rules processing system using associative memory for pipelined execution of plural join operations and concurrent condition comparing
US4907167A (en) * 1987-09-30 1990-03-06 E. I. Du Pont De Nemours And Company Process control system with action logging
US4910691A (en) * 1987-09-30 1990-03-20 E.I. Du Pont De Nemours & Co. Process control system with multiple module sequence options
US4916628A (en) * 1988-07-08 1990-04-10 Commonwealth Edison Company Microprocessor-based control/status monitoring arrangement
US4931950A (en) * 1988-07-25 1990-06-05 Electric Power Research Institute Multimedia interface and method for computer system
US4967337A (en) * 1988-10-11 1990-10-30 Texas Instruments Incorporated Automated diagnostic system
US4975865A (en) * 1989-05-31 1990-12-04 Mitech Corporation Method and apparatus for real-time control
US4979124A (en) * 1988-10-05 1990-12-18 Cornell Research Foundation Adaptive, neural-based signal processor
US4985857A (en) * 1988-08-19 1991-01-15 General Motors Corporation Method and apparatus for diagnosing machines
US5014220A (en) * 1988-09-06 1991-05-07 The Boeing Company Reliability model generator
US5058043A (en) * 1989-04-05 1991-10-15 E. I. Du Pont De Nemours & Co. (Inc.) Batch process control using expert systems
US5063527A (en) * 1987-01-29 1991-11-05 British Gas Plc Monitor system
US5067099A (en) * 1988-11-03 1991-11-19 Allied-Signal Inc. Methods and apparatus for monitoring system performance
US5070468A (en) * 1988-07-20 1991-12-03 Mitsubishi Jukogyo Kabushiki Kaisha Plant fault diagnosis system
US5081598A (en) * 1989-02-21 1992-01-14 Westinghouse Electric Corp. Method for associating text in automatic diagnostic system to produce recommended actions automatically
US5090014A (en) * 1988-03-30 1992-02-18 Digital Equipment Corporation Identifying likely failure points in a digital data processing system
US5089978A (en) * 1990-02-09 1992-02-18 Westinghouse Electric Corp. Automatic plant state diagnosis system including a display selection system for selecting displays responsive to the diagnosis
US5099436A (en) * 1988-11-03 1992-03-24 Allied-Signal Inc. Methods and apparatus for performing system fault diagnosis
US5101337A (en) * 1988-11-09 1992-03-31 Mitsubishi Denki Kabushiki Kaisha Plant diagnostic apparatus
US5119318A (en) * 1989-04-17 1992-06-02 Del Partners L.P. Expert control system for real time management of automated factory equipment
US5121496A (en) * 1988-07-25 1992-06-09 Westinghouse Electric Corp. Method for creating, maintaining and using an expert system by recursively modifying calibration file and merging with standard file
US5122976A (en) * 1990-03-12 1992-06-16 Westinghouse Electric Corp. Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses
US5130936A (en) * 1990-09-14 1992-07-14 Arinc Research Corporation Method and apparatus for diagnostic testing including a neural network for determining testing sufficiency
US5132920A (en) * 1988-02-16 1992-07-21 Westinghouse Electric Corp. Automated system to prioritize repair of plant equipment
US5193143A (en) * 1988-01-12 1993-03-09 Honeywell Inc. Problem state monitoring
US5228116A (en) * 1988-07-15 1993-07-13 Aicorp., Inc. Knowledge base management system
US5265035A (en) * 1992-05-18 1993-11-23 The University Of Chicago System diagnostics using qualitative analysis and component functional classification
US5274572A (en) * 1987-12-02 1993-12-28 Schlumberger Technology Corporation Method and apparatus for knowledge-based signal monitoring and analysis
US5293323A (en) * 1991-10-24 1994-03-08 General Electric Company Method for fault diagnosis by assessment of confidence measure
US5305426A (en) * 1991-05-15 1994-04-19 Kabushiki Kaisha Toshiba Plant operation support system for diagnosing malfunction of plant
US5329465A (en) * 1987-10-30 1994-07-12 Westinghouse Electric Corp. Online valve diagnostic monitoring system
US5345383A (en) * 1992-09-16 1994-09-06 Caterpillar Inc. Method and apparatus for selectively monitoring input
US5351200A (en) * 1991-11-22 1994-09-27 Westinghouse Electric Corporation Process facility monitor using fuzzy logic
US5406476A (en) * 1991-04-11 1995-04-11 Sun Microsystems, Inc. Method and apparatus for resource constraint scheduling
US5406502A (en) * 1993-06-29 1995-04-11 Elbit Ltd. System and method for measuring the operation of a device
US5418887A (en) * 1989-05-08 1995-05-23 U.S. Philips Corporation Data processing apparatus for a temporal based rule system
US5442555A (en) * 1992-05-18 1995-08-15 Argonne National Laboratory Combined expert system/neural networks method for process fault diagnosis
US5469463A (en) * 1988-03-30 1995-11-21 Digital Equipment Corporation Expert system for identifying likely failure points in a digital data processing system
US5479560A (en) * 1992-10-30 1995-12-26 Technology Research Association Of Medical And Welfare Apparatus Formant detecting device and speech processing apparatus
US5488697A (en) * 1988-01-12 1996-01-30 Honeywell Inc. Problem state monitoring system
US5490089A (en) * 1993-06-15 1996-02-06 Xerox Corporation Interactive user support system and method using sensors and machine knowledge
US5511004A (en) * 1992-06-03 1996-04-23 Thomson-Csf Diagnostic method for an evolutionary process
US5550751A (en) * 1993-10-15 1996-08-27 The Texas A & M University System Expert system for detecting high impedance faults
US5566091A (en) * 1994-06-30 1996-10-15 Caterpillar Inc. Method and apparatus for machine health inference by comparing two like loaded components
US5598511A (en) * 1992-12-28 1997-01-28 Intel Corporation Method and apparatus for interpreting data and accessing on-line documentation in a computer system
US5608845A (en) * 1989-03-17 1997-03-04 Hitachi, Ltd. Method for diagnosing a remaining lifetime, apparatus for diagnosing a remaining lifetime, method for displaying remaining lifetime data, display apparatus and expert system
US5629862A (en) * 1994-08-30 1997-05-13 Electric Power Research Institute Rule-based procedure for automatic selection of contingencies in evaluation of dynamic security of a power distribution system
US5696696A (en) * 1994-10-11 1997-12-09 Betzdearborn, Inc. Apparatus and method for automatically achieving and maintaining congruent control in an industrial boiler
US5710723A (en) * 1995-04-05 1998-01-20 Dayton T. Brown Method and apparatus for performing pre-emptive maintenance on operating equipment
US5748495A (en) * 1989-12-22 1998-05-05 Hitachi, Ltd. Plant operating and monitoring apparatus
US5835886A (en) * 1994-10-26 1998-11-10 Siemens Aktiengesellschaft Method for analyzing a measurement value and measurement value analyzer for carrying out the method
US5845272A (en) * 1996-11-29 1998-12-01 General Electric Company System and method for isolating failures in a locomotive
US5905989A (en) * 1996-11-27 1999-05-18 Bently Nevada Corporation Knowledge manager relying on a hierarchical default expert system: apparatus and method
US5923571A (en) * 1994-10-11 1999-07-13 Betzdearborn, Inc. Apparatus and method for automatic congruent control of multiple boilers sharing a common feedwater line and chemical feed point
US5950147A (en) * 1997-06-05 1999-09-07 Caterpillar Inc. Method and apparatus for predicting a fault condition
US5956663A (en) * 1996-11-07 1999-09-21 Rosemount, Inc. Signal processing technique which separates signal components in a sensor for sensor diagnostics
US6017143A (en) * 1996-03-28 2000-01-25 Rosemount Inc. Device in a process system for detecting events
US6047220A (en) * 1996-12-31 2000-04-04 Rosemount Inc. Device in a process system for validating a control signal from a field device
US6175934B1 (en) 1997-12-15 2001-01-16 General Electric Company Method and apparatus for enhanced service quality through remote diagnostics
US6298454B1 (en) 1999-02-22 2001-10-02 Fisher-Rosemount Systems, Inc. Diagnostics in a process control system
US6356191B1 (en) 1999-06-17 2002-03-12 Rosemount Inc. Error compensation for a process fluid temperature transmitter
US6356857B1 (en) * 1998-08-17 2002-03-12 Aspen Technology, Inc. Sensor validation apparatus and method
US6363330B1 (en) 1998-04-10 2002-03-26 Satnam Singh Sampuran Alag Thermocouple failure detection in power generation turbines
US6370448B1 (en) 1997-10-13 2002-04-09 Rosemount Inc. Communication technique for field devices in industrial processes
US20020066054A1 (en) * 2000-06-12 2002-05-30 Jaw Link C. Fault detection in a physical system
US6421571B1 (en) 2000-02-29 2002-07-16 Bently Nevada Corporation Industrial plant asset management system: apparatus and method
US6434504B1 (en) 1996-11-07 2002-08-13 Rosemount Inc. Resistance based process control device diagnostics
US6446027B1 (en) * 1999-09-17 2002-09-03 General Electric Company Intelligent analysis system and method for fluid-filled electrical equipment
US6449574B1 (en) 1996-11-07 2002-09-10 Micro Motion, Inc. Resistance based process control device diagnostics
US6473710B1 (en) 1999-07-01 2002-10-29 Rosemount Inc. Low power two-wire self validating temperature transmitter
US6494617B1 (en) 1999-04-30 2002-12-17 General Electric Company Status detection apparatus and method for fluid-filled electrical equipment
US6505517B1 (en) 1999-07-23 2003-01-14 Rosemount Inc. High accuracy signal processing for magnetic flowmeter
US6519546B1 (en) 1996-11-07 2003-02-11 Rosemount Inc. Auto correcting temperature transmitter with resistance based sensor
US6539267B1 (en) 1996-03-28 2003-03-25 Rosemount Inc. Device in a process system for determining statistical parameter
US6556145B1 (en) 1999-09-24 2003-04-29 Rosemount Inc. Two-wire fluid temperature transmitter with thermocouple diagnostics
US6601005B1 (en) 1996-11-07 2003-07-29 Rosemount Inc. Process device diagnostics using process variable sensor signal
US20030154051A1 (en) * 2002-02-13 2003-08-14 Kabushiki Kaisha Toshiba Method and system for diagnosis of plant
US6611775B1 (en) 1998-12-10 2003-08-26 Rosemount Inc. Electrode leakage diagnostics in a magnetic flow meter
US6615149B1 (en) 1998-12-10 2003-09-02 Rosemount Inc. Spectral diagnostics in a magnetic flow meter
US6629059B2 (en) 2001-05-14 2003-09-30 Fisher-Rosemount Systems, Inc. Hand held diagnostic and communication device with automatic bus detection
US6633782B1 (en) 1999-02-22 2003-10-14 Fisher-Rosemount Systems, Inc. Diagnostic expert in a process control system
US6654697B1 (en) 1996-03-28 2003-11-25 Rosemount Inc. Flow measurement with diagnostics
US20040024568A1 (en) * 1999-06-25 2004-02-05 Evren Eryurek Process device diagnostics using process variable sensor signal
US6701274B1 (en) 1999-08-27 2004-03-02 Rosemount Inc. Prediction of error magnitude in a pressure transmitter
US6735484B1 (en) 2000-09-20 2004-05-11 Fargo Electronics, Inc. Printer with a process diagnostics system for detecting events
US6754601B1 (en) 1996-11-07 2004-06-22 Rosemount Inc. Diagnostics for resistive elements of process devices
US6772036B2 (en) 2001-08-30 2004-08-03 Fisher-Rosemount Systems, Inc. Control system using process model
US20040249583A1 (en) * 1996-03-28 2004-12-09 Evren Eryurek Pressure transmitter with diagnostics
US20050011278A1 (en) * 2003-07-18 2005-01-20 Brown Gregory C. Process diagnostics
US20050030185A1 (en) * 2003-08-07 2005-02-10 Huisenga Garrie D. Process device with quiescent current diagnostics
US20050072239A1 (en) * 2003-09-30 2005-04-07 Longsdorf Randy J. Process device with vibration based diagnostics
US6907383B2 (en) 1996-03-28 2005-06-14 Rosemount Inc. Flow diagnostic system
US20050132808A1 (en) * 2003-12-23 2005-06-23 Brown Gregory C. Diagnostics of impulse piping in an industrial process
US6920799B1 (en) 2004-04-15 2005-07-26 Rosemount Inc. Magnetic flow meter with reference electrode
US6934696B1 (en) 2000-09-15 2005-08-23 Bently Nevada, Llc Custom rule system and method for expert systems
US20050210337A1 (en) * 2004-03-04 2005-09-22 Falconeer Technologies Llc Method and system of monitoring, sensor validation and predictive fault analysis
US6970003B2 (en) 2001-03-05 2005-11-29 Rosemount Inc. Electronics board life prediction of microprocessor-based transmitters
US20060036404A1 (en) * 1996-03-28 2006-02-16 Wiklund David E Process variable transmitter with diagnostics
US20060095394A1 (en) * 1996-03-28 2006-05-04 Miller John P Rule set for root cause diagnostics
US7046180B2 (en) 2004-04-21 2006-05-16 Rosemount Inc. Analog-to-digital converter with range error detection
US7085610B2 (en) 1996-03-28 2006-08-01 Fisher-Rosemount Systems, Inc. Root cause diagnostics
US20060277000A1 (en) * 1996-03-28 2006-12-07 Wehrs David L Flow measurement diagnostics
US20060282580A1 (en) * 2005-06-08 2006-12-14 Russell Alden C Iii Multi-protocol field device interface with automatic bus detection
US20070010968A1 (en) * 1996-03-28 2007-01-11 Longsdorf Randy J Dedicated process diagnostic device
US20070068225A1 (en) * 2005-09-29 2007-03-29 Brown Gregory C Leak detector for process valve
US20070150305A1 (en) * 2004-02-18 2007-06-28 Klaus Abraham-Fuchs Method for selecting a potential participant for a medical study on the basis of a selection criterion
US7321846B1 (en) 2006-10-05 2008-01-22 Rosemount Inc. Two-wire process control loop diagnostics
US20080125884A1 (en) * 2006-09-26 2008-05-29 Schumacher Mark S Automatic field device service adviser
US20090043530A1 (en) * 2007-08-06 2009-02-12 Sittler Fred C Process variable transmitter with acceleration sensor
US20090083001A1 (en) * 2007-09-25 2009-03-26 Huisenga Garrie D Field device for digital process control loop diagnostics
US20090266076A1 (en) * 2008-04-28 2009-10-29 Siemens Energy, Inc. Condensate Polisher Circuit
US20090276136A1 (en) * 2008-04-30 2009-11-05 Steven Wayne Butler Method for calculating confidence on prediction in fault diagnosis systems
US20100017092A1 (en) * 2008-07-16 2010-01-21 Steven Wayne Butler Hybrid fault isolation system utilizing both model-based and empirical components
US7750642B2 (en) 2006-09-29 2010-07-06 Rosemount Inc. Magnetic flowmeter with verification
US20100199671A1 (en) * 2009-02-06 2010-08-12 Siemens Energy, Inc. Deaerator Apparatus in a Superatmospheric Condenser System
US20100288054A1 (en) * 2009-05-12 2010-11-18 Foss Scot R System to detect poor process ground connections
US7953501B2 (en) 2006-09-25 2011-05-31 Fisher-Rosemount Systems, Inc. Industrial process control loop monitor
FR2956701A1 (fr) * 2010-02-24 2011-08-26 Snecma Systeme de detection et de localisation de pannes et moteur comportant un tel systeme
US20120040299A1 (en) * 2010-08-16 2012-02-16 Emerson Process Management Power & Water Solutions, Inc. Dynamic matrix control of steam temperature with prevention of saturated steam entry into superheater
US20140115404A1 (en) * 1997-05-13 2014-04-24 Round Rock Research Llc Diagnostic and managing distributed processor system
US8964338B2 (en) 2012-01-11 2015-02-24 Emerson Climate Technologies, Inc. System and method for compressor motor protection
US8974573B2 (en) 2004-08-11 2015-03-10 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US9052240B2 (en) 2012-06-29 2015-06-09 Rosemount Inc. Industrial process temperature transmitter with sensor stress diagnostics
US9121407B2 (en) 2004-04-27 2015-09-01 Emerson Climate Technologies, Inc. Compressor diagnostic and protection system and method
US9140728B2 (en) 2007-11-02 2015-09-22 Emerson Climate Technologies, Inc. Compressor sensor module
US9163828B2 (en) 2011-10-31 2015-10-20 Emerson Process Management Power & Water Solutions, Inc. Model-based load demand control
US9207670B2 (en) 2011-03-21 2015-12-08 Rosemount Inc. Degrading sensor detection implemented within a transmitter
US9207129B2 (en) 2012-09-27 2015-12-08 Rosemount Inc. Process variable transmitter with EMF detection and correction
US9285802B2 (en) 2011-02-28 2016-03-15 Emerson Electric Co. Residential solutions HVAC monitoring and diagnosis
US9310094B2 (en) 2007-07-30 2016-04-12 Emerson Climate Technologies, Inc. Portable method and apparatus for monitoring refrigerant-cycle systems
US9310439B2 (en) 2012-09-25 2016-04-12 Emerson Climate Technologies, Inc. Compressor having a control and diagnostic module
US20160113588A1 (en) * 2013-07-12 2016-04-28 Seiko Epson Corporation Biological information detection device
US9335042B2 (en) 2010-08-16 2016-05-10 Emerson Process Management Power & Water Solutions, Inc. Steam temperature control using dynamic matrix control
US9447963B2 (en) 2010-08-16 2016-09-20 Emerson Process Management Power & Water Solutions, Inc. Dynamic tuning of dynamic matrix control of steam temperature
US9551504B2 (en) 2013-03-15 2017-01-24 Emerson Electric Co. HVAC system remote monitoring and diagnosis
US9602122B2 (en) 2012-09-28 2017-03-21 Rosemount Inc. Process variable measurement noise diagnostic
US9638436B2 (en) 2013-03-15 2017-05-02 Emerson Electric Co. HVAC system remote monitoring and diagnosis
US9689823B2 (en) 2015-03-10 2017-06-27 Rosemount Inc. Steam quality meter and measurement method
US9765979B2 (en) 2013-04-05 2017-09-19 Emerson Climate Technologies, Inc. Heat-pump system with refrigerant charge diagnostics
US9803902B2 (en) 2013-03-15 2017-10-31 Emerson Climate Technologies, Inc. System for refrigerant charge verification using two condenser coil temperatures
US9823632B2 (en) 2006-09-07 2017-11-21 Emerson Climate Technologies, Inc. Compressor data module
US9885507B2 (en) 2006-07-19 2018-02-06 Emerson Climate Technologies, Inc. Protection and diagnostic module for a refrigeration system
CN108490238A (zh) * 2018-03-09 2018-09-04 中广核核电运营有限公司 汽轮发电机轴电流报警数据处理装置及方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4713772A (en) * 1985-11-18 1987-12-15 Westinghouse Electric Corp. Automatic on-line chemistry monitoring system having improved calibration unit
US4935195A (en) * 1988-08-29 1990-06-19 Westinghouse Electric Corp. Corrosion-erosion trend monitoring and diagnostic system
DE59308748D1 (de) * 1992-05-27 1998-08-13 Elin Energieversorgung Verfahren zur verarbeitung von mittels aktiver und passiver aufnehmer erfasster analoger und binärer messgrössen in einer vorwiegend elektrische energie erzeugenden anlage

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4302813A (en) * 1978-02-22 1981-11-24 Hitachi, Ltd. Method of controlling operation of rotary machines by diagnosing abnormal conditions
US4328556A (en) * 1975-09-09 1982-05-04 Tokyo Denryoku Kabushiki Kaisha Control system of plants by means of electronic computers
US4389706A (en) * 1972-05-03 1983-06-21 Westinghouse Electric Corp. Digital computer monitored and/or operated system or process which is structured for operation with an improved automatic programming process and system
US4404637A (en) * 1981-04-30 1983-09-13 Phillips Petroleum Company Process control system
US4471446A (en) * 1982-07-12 1984-09-11 Westinghouse Electric Corp. Control system and method for a steam turbine having a steam bypass arrangement
US4517468A (en) * 1984-04-30 1985-05-14 Westinghouse Electric Corp. Diagnostic system and method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2824190A1 (de) * 1978-06-02 1979-12-06 Bosch Gmbh Robert Mikrorechner-system zur steuerung von betriebsvorgaengen in kraftfahrzeugen, mit einer diagnoseeinrichtung zur ueberpruefung des kraftfahrzeuges
US4337516A (en) * 1980-06-26 1982-06-29 United Technologies Corporation Sensor fault detection by activity monitoring
GB2083258B (en) * 1980-09-03 1984-07-25 Nuclear Power Co Ltd Alarm systems
JPS59229622A (ja) * 1983-06-10 1984-12-24 Toshiba Corp プラント診断装置
JPH0619666B2 (ja) * 1983-06-30 1994-03-16 富士通株式会社 故障診断処理方式

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4389706A (en) * 1972-05-03 1983-06-21 Westinghouse Electric Corp. Digital computer monitored and/or operated system or process which is structured for operation with an improved automatic programming process and system
US4328556A (en) * 1975-09-09 1982-05-04 Tokyo Denryoku Kabushiki Kaisha Control system of plants by means of electronic computers
US4302813A (en) * 1978-02-22 1981-11-24 Hitachi, Ltd. Method of controlling operation of rotary machines by diagnosing abnormal conditions
US4404637A (en) * 1981-04-30 1983-09-13 Phillips Petroleum Company Process control system
US4471446A (en) * 1982-07-12 1984-09-11 Westinghouse Electric Corp. Control system and method for a steam turbine having a steam bypass arrangement
US4517468A (en) * 1984-04-30 1985-05-14 Westinghouse Electric Corp. Diagnostic system and method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Fox et al: Techniques for Sensor Based Diagnosis, Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI 83, vol. 1, pp. 158 163. *
Fox et al: Techniques for Sensor Based Diagnosis, Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI-83, vol. 1, pp. 158-163.

Cited By (223)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4901229A (en) * 1985-01-21 1990-02-13 Tsutomu Tashiro Parallelized rules processing system using associative memory for pipelined execution of plural join operations and concurrent condition comparing
US4839823A (en) * 1985-03-20 1989-06-13 Kabushiki Kaisha Toshiba Automatic trouble analysis apparatus and method thereof
US4754409A (en) * 1985-06-26 1988-06-28 International Business Machines Corporation Method for dynamically collecting current data from specified external processes and procedures for use in an expert system
US4766550A (en) * 1985-10-30 1988-08-23 Westinghouse Electric Corp. Automatic on-line chemistry monitoring system
US4763277A (en) * 1986-01-17 1988-08-09 International Business Machines Corporation Method for obtaining information in an expert system
US4792911A (en) * 1986-01-17 1988-12-20 Westinghouse Electric Corp. Diagnostic apparatus for an electric generator seal oil system
US4754410A (en) * 1986-02-06 1988-06-28 Westinghouse Electric Corp. Automated rule based process control method with feedback and apparatus therefor
US4783752A (en) * 1986-03-06 1988-11-08 Teknowledge, Inc. Knowledge based processor for application programs using conventional data processing capabilities
US4825195A (en) * 1986-05-07 1989-04-25 Yves Berruyer Method of monitoring to anticipate the triggering of an alarm
US4841456A (en) * 1986-09-09 1989-06-20 The Boeing Company Test system and method using artificial intelligence control
US5063527A (en) * 1987-01-29 1991-11-05 British Gas Plc Monitor system
US4839822A (en) * 1987-08-13 1989-06-13 501 Synthes (U.S.A.) Computer system and method for suggesting treatments for physical trauma
US4910691A (en) * 1987-09-30 1990-03-20 E.I. Du Pont De Nemours & Co. Process control system with multiple module sequence options
US4907167A (en) * 1987-09-30 1990-03-06 E. I. Du Pont De Nemours And Company Process control system with action logging
US5329465A (en) * 1987-10-30 1994-07-12 Westinghouse Electric Corp. Online valve diagnostic monitoring system
US5274572A (en) * 1987-12-02 1993-12-28 Schlumberger Technology Corporation Method and apparatus for knowledge-based signal monitoring and analysis
US5488697A (en) * 1988-01-12 1996-01-30 Honeywell Inc. Problem state monitoring system
US5193143A (en) * 1988-01-12 1993-03-09 Honeywell Inc. Problem state monitoring
US5132920A (en) * 1988-02-16 1992-07-21 Westinghouse Electric Corp. Automated system to prioritize repair of plant equipment
US4885705A (en) * 1988-02-25 1989-12-05 Eastman Kodak Company Expert system shell for building photofinishing diagnostic systems
US5469463A (en) * 1988-03-30 1995-11-21 Digital Equipment Corporation Expert system for identifying likely failure points in a digital data processing system
US5090014A (en) * 1988-03-30 1992-02-18 Digital Equipment Corporation Identifying likely failure points in a digital data processing system
EP0347645A3 (en) * 1988-06-22 1992-08-12 Westinghouse Electric Corporation Monitoring a plurality of parameters from identical processes
EP0347645A2 (en) * 1988-06-22 1989-12-27 Westinghouse Electric Corporation Monitoring a plurality of parameters from identical processes
US4951234A (en) * 1988-06-22 1990-08-21 Westinghouse Electric Corp. Monitoring a plurality of identical process parameters
US4916628A (en) * 1988-07-08 1990-04-10 Commonwealth Edison Company Microprocessor-based control/status monitoring arrangement
US5228116A (en) * 1988-07-15 1993-07-13 Aicorp., Inc. Knowledge base management system
US5070468A (en) * 1988-07-20 1991-12-03 Mitsubishi Jukogyo Kabushiki Kaisha Plant fault diagnosis system
US4931950A (en) * 1988-07-25 1990-06-05 Electric Power Research Institute Multimedia interface and method for computer system
US5121496A (en) * 1988-07-25 1992-06-09 Westinghouse Electric Corp. Method for creating, maintaining and using an expert system by recursively modifying calibration file and merging with standard file
US4985857A (en) * 1988-08-19 1991-01-15 General Motors Corporation Method and apparatus for diagnosing machines
US5014220A (en) * 1988-09-06 1991-05-07 The Boeing Company Reliability model generator
US4979124A (en) * 1988-10-05 1990-12-18 Cornell Research Foundation Adaptive, neural-based signal processor
US4967337A (en) * 1988-10-11 1990-10-30 Texas Instruments Incorporated Automated diagnostic system
US5099436A (en) * 1988-11-03 1992-03-24 Allied-Signal Inc. Methods and apparatus for performing system fault diagnosis
US5067099A (en) * 1988-11-03 1991-11-19 Allied-Signal Inc. Methods and apparatus for monitoring system performance
US5101337A (en) * 1988-11-09 1992-03-31 Mitsubishi Denki Kabushiki Kaisha Plant diagnostic apparatus
US5081598A (en) * 1989-02-21 1992-01-14 Westinghouse Electric Corp. Method for associating text in automatic diagnostic system to produce recommended actions automatically
US5608845A (en) * 1989-03-17 1997-03-04 Hitachi, Ltd. Method for diagnosing a remaining lifetime, apparatus for diagnosing a remaining lifetime, method for displaying remaining lifetime data, display apparatus and expert system
US5058043A (en) * 1989-04-05 1991-10-15 E. I. Du Pont De Nemours & Co. (Inc.) Batch process control using expert systems
US5119318A (en) * 1989-04-17 1992-06-02 Del Partners L.P. Expert control system for real time management of automated factory equipment
US5418887A (en) * 1989-05-08 1995-05-23 U.S. Philips Corporation Data processing apparatus for a temporal based rule system
US4975865A (en) * 1989-05-31 1990-12-04 Mitech Corporation Method and apparatus for real-time control
US5777896A (en) * 1989-12-22 1998-07-07 Hitachi, Ltd. Plant operating and monitoring apparatus
US5748495A (en) * 1989-12-22 1998-05-05 Hitachi, Ltd. Plant operating and monitoring apparatus
US5089978A (en) * 1990-02-09 1992-02-18 Westinghouse Electric Corp. Automatic plant state diagnosis system including a display selection system for selecting displays responsive to the diagnosis
US5122976A (en) * 1990-03-12 1992-06-16 Westinghouse Electric Corp. Method and apparatus for remotely controlling sensor processing algorithms to expert sensor diagnoses
US5130936A (en) * 1990-09-14 1992-07-14 Arinc Research Corporation Method and apparatus for diagnostic testing including a neural network for determining testing sufficiency
US5406476A (en) * 1991-04-11 1995-04-11 Sun Microsystems, Inc. Method and apparatus for resource constraint scheduling
US5305426A (en) * 1991-05-15 1994-04-19 Kabushiki Kaisha Toshiba Plant operation support system for diagnosing malfunction of plant
US5293323A (en) * 1991-10-24 1994-03-08 General Electric Company Method for fault diagnosis by assessment of confidence measure
US5351200A (en) * 1991-11-22 1994-09-27 Westinghouse Electric Corporation Process facility monitor using fuzzy logic
US5442555A (en) * 1992-05-18 1995-08-15 Argonne National Laboratory Combined expert system/neural networks method for process fault diagnosis
US5265035A (en) * 1992-05-18 1993-11-23 The University Of Chicago System diagnostics using qualitative analysis and component functional classification
US5511004A (en) * 1992-06-03 1996-04-23 Thomson-Csf Diagnostic method for an evolutionary process
US5345383A (en) * 1992-09-16 1994-09-06 Caterpillar Inc. Method and apparatus for selectively monitoring input
US5479560A (en) * 1992-10-30 1995-12-26 Technology Research Association Of Medical And Welfare Apparatus Formant detecting device and speech processing apparatus
US5598511A (en) * 1992-12-28 1997-01-28 Intel Corporation Method and apparatus for interpreting data and accessing on-line documentation in a computer system
US5490089A (en) * 1993-06-15 1996-02-06 Xerox Corporation Interactive user support system and method using sensors and machine knowledge
US5406502A (en) * 1993-06-29 1995-04-11 Elbit Ltd. System and method for measuring the operation of a device
US5550751A (en) * 1993-10-15 1996-08-27 The Texas A & M University System Expert system for detecting high impedance faults
US5566091A (en) * 1994-06-30 1996-10-15 Caterpillar Inc. Method and apparatus for machine health inference by comparing two like loaded components
US5629862A (en) * 1994-08-30 1997-05-13 Electric Power Research Institute Rule-based procedure for automatic selection of contingencies in evaluation of dynamic security of a power distribution system
US5923571A (en) * 1994-10-11 1999-07-13 Betzdearborn, Inc. Apparatus and method for automatic congruent control of multiple boilers sharing a common feedwater line and chemical feed point
US5696696A (en) * 1994-10-11 1997-12-09 Betzdearborn, Inc. Apparatus and method for automatically achieving and maintaining congruent control in an industrial boiler
US5835886A (en) * 1994-10-26 1998-11-10 Siemens Aktiengesellschaft Method for analyzing a measurement value and measurement value analyzer for carrying out the method
US5710723A (en) * 1995-04-05 1998-01-20 Dayton T. Brown Method and apparatus for performing pre-emptive maintenance on operating equipment
US7085610B2 (en) 1996-03-28 2006-08-01 Fisher-Rosemount Systems, Inc. Root cause diagnostics
US6397114B1 (en) 1996-03-28 2002-05-28 Rosemount Inc. Device in a process system for detecting events
US6654697B1 (en) 1996-03-28 2003-11-25 Rosemount Inc. Flow measurement with diagnostics
US6539267B1 (en) 1996-03-28 2003-03-25 Rosemount Inc. Device in a process system for determining statistical parameter
US6017143A (en) * 1996-03-28 2000-01-25 Rosemount Inc. Device in a process system for detecting events
US6532392B1 (en) 1996-03-28 2003-03-11 Rosemount Inc. Transmitter with software for determining when to initiate diagnostics
US6119047A (en) * 1996-03-28 2000-09-12 Rosemount Inc. Transmitter with software for determining when to initiate diagnostics
US8290721B2 (en) 1996-03-28 2012-10-16 Rosemount Inc. Flow measurement diagnostics
US7949495B2 (en) 1996-03-28 2011-05-24 Rosemount, Inc. Process variable transmitter with diagnostics
US7630861B2 (en) 1996-03-28 2009-12-08 Rosemount Inc. Dedicated process diagnostic device
US7623932B2 (en) 1996-03-28 2009-11-24 Fisher-Rosemount Systems, Inc. Rule set for root cause diagnostics
US7254518B2 (en) 1996-03-28 2007-08-07 Rosemount Inc. Pressure transmitter with diagnostics
US20070010968A1 (en) * 1996-03-28 2007-01-11 Longsdorf Randy J Dedicated process diagnostic device
US20040249583A1 (en) * 1996-03-28 2004-12-09 Evren Eryurek Pressure transmitter with diagnostics
US20060277000A1 (en) * 1996-03-28 2006-12-07 Wehrs David L Flow measurement diagnostics
US6907383B2 (en) 1996-03-28 2005-06-14 Rosemount Inc. Flow diagnostic system
US20060036404A1 (en) * 1996-03-28 2006-02-16 Wiklund David E Process variable transmitter with diagnostics
US20060095394A1 (en) * 1996-03-28 2006-05-04 Miller John P Rule set for root cause diagnostics
US6449574B1 (en) 1996-11-07 2002-09-10 Micro Motion, Inc. Resistance based process control device diagnostics
US6434504B1 (en) 1996-11-07 2002-08-13 Rosemount Inc. Resistance based process control device diagnostics
US6601005B1 (en) 1996-11-07 2003-07-29 Rosemount Inc. Process device diagnostics using process variable sensor signal
US6754601B1 (en) 1996-11-07 2004-06-22 Rosemount Inc. Diagnostics for resistive elements of process devices
US6519546B1 (en) 1996-11-07 2003-02-11 Rosemount Inc. Auto correcting temperature transmitter with resistance based sensor
US5956663A (en) * 1996-11-07 1999-09-21 Rosemount, Inc. Signal processing technique which separates signal components in a sensor for sensor diagnostics
US5905989A (en) * 1996-11-27 1999-05-18 Bently Nevada Corporation Knowledge manager relying on a hierarchical default expert system: apparatus and method
US5845272A (en) * 1996-11-29 1998-12-01 General Electric Company System and method for isolating failures in a locomotive
US6047220A (en) * 1996-12-31 2000-04-04 Rosemount Inc. Device in a process system for validating a control signal from a field device
US20140115404A1 (en) * 1997-05-13 2014-04-24 Round Rock Research Llc Diagnostic and managing distributed processor system
US9348722B2 (en) * 1997-05-13 2016-05-24 Round Rock Research, Llc Diagnostic and managing distributed processor system
US5950147A (en) * 1997-06-05 1999-09-07 Caterpillar Inc. Method and apparatus for predicting a fault condition
US6370448B1 (en) 1997-10-13 2002-04-09 Rosemount Inc. Communication technique for field devices in industrial processes
US6175934B1 (en) 1997-12-15 2001-01-16 General Electric Company Method and apparatus for enhanced service quality through remote diagnostics
US6363330B1 (en) 1998-04-10 2002-03-26 Satnam Singh Sampuran Alag Thermocouple failure detection in power generation turbines
US6356857B1 (en) * 1998-08-17 2002-03-12 Aspen Technology, Inc. Sensor validation apparatus and method
US6594603B1 (en) 1998-10-19 2003-07-15 Rosemount Inc. Resistive element diagnostics for process devices
US6615149B1 (en) 1998-12-10 2003-09-02 Rosemount Inc. Spectral diagnostics in a magnetic flow meter
US6611775B1 (en) 1998-12-10 2003-08-26 Rosemount Inc. Electrode leakage diagnostics in a magnetic flow meter
US6615090B1 (en) 1999-02-22 2003-09-02 Fisher-Rosemont Systems, Inc. Diagnostics in a process control system which uses multi-variable control techniques
US6633782B1 (en) 1999-02-22 2003-10-14 Fisher-Rosemount Systems, Inc. Diagnostic expert in a process control system
US6557118B2 (en) 1999-02-22 2003-04-29 Fisher Rosemount Systems Inc. Diagnostics in a process control system
US6298454B1 (en) 1999-02-22 2001-10-02 Fisher-Rosemount Systems, Inc. Diagnostics in a process control system
US6494617B1 (en) 1999-04-30 2002-12-17 General Electric Company Status detection apparatus and method for fluid-filled electrical equipment
US6356191B1 (en) 1999-06-17 2002-03-12 Rosemount Inc. Error compensation for a process fluid temperature transmitter
US7010459B2 (en) 1999-06-25 2006-03-07 Rosemount Inc. Process device diagnostics using process variable sensor signal
US20040024568A1 (en) * 1999-06-25 2004-02-05 Evren Eryurek Process device diagnostics using process variable sensor signal
US6473710B1 (en) 1999-07-01 2002-10-29 Rosemount Inc. Low power two-wire self validating temperature transmitter
US6505517B1 (en) 1999-07-23 2003-01-14 Rosemount Inc. High accuracy signal processing for magnetic flowmeter
US6701274B1 (en) 1999-08-27 2004-03-02 Rosemount Inc. Prediction of error magnitude in a pressure transmitter
US6446027B1 (en) * 1999-09-17 2002-09-03 General Electric Company Intelligent analysis system and method for fluid-filled electrical equipment
US6556145B1 (en) 1999-09-24 2003-04-29 Rosemount Inc. Two-wire fluid temperature transmitter with thermocouple diagnostics
US6889096B2 (en) * 2000-02-29 2005-05-03 Bently Nevada, Llc Industrial plant asset management system: apparatus and method
US6421571B1 (en) 2000-02-29 2002-07-16 Bently Nevada Corporation Industrial plant asset management system: apparatus and method
US6775576B2 (en) 2000-02-29 2004-08-10 Bently Nevada, Llc Industrial plant asset management system: apparatus and method
US20030028269A1 (en) * 2000-02-29 2003-02-06 Bob Spriggs Industrial plant asset management system: apparatus and method
US6898554B2 (en) * 2000-06-12 2005-05-24 Scientific Monitoring, Inc. Fault detection in a physical system
US20020066054A1 (en) * 2000-06-12 2002-05-30 Jaw Link C. Fault detection in a physical system
US6934696B1 (en) 2000-09-15 2005-08-23 Bently Nevada, Llc Custom rule system and method for expert systems
US6735484B1 (en) 2000-09-20 2004-05-11 Fargo Electronics, Inc. Printer with a process diagnostics system for detecting events
US6970003B2 (en) 2001-03-05 2005-11-29 Rosemount Inc. Electronics board life prediction of microprocessor-based transmitters
US6629059B2 (en) 2001-05-14 2003-09-30 Fisher-Rosemount Systems, Inc. Hand held diagnostic and communication device with automatic bus detection
US6772036B2 (en) 2001-08-30 2004-08-03 Fisher-Rosemount Systems, Inc. Control system using process model
US20030154051A1 (en) * 2002-02-13 2003-08-14 Kabushiki Kaisha Toshiba Method and system for diagnosis of plant
US6909990B2 (en) * 2002-02-13 2005-06-21 Kabushiki Kaisha Toshiba Method and system for diagnosis of plant
US20050011278A1 (en) * 2003-07-18 2005-01-20 Brown Gregory C. Process diagnostics
US7290450B2 (en) 2003-07-18 2007-11-06 Rosemount Inc. Process diagnostics
US7018800B2 (en) 2003-08-07 2006-03-28 Rosemount Inc. Process device with quiescent current diagnostics
US20050030185A1 (en) * 2003-08-07 2005-02-10 Huisenga Garrie D. Process device with quiescent current diagnostics
US20050072239A1 (en) * 2003-09-30 2005-04-07 Longsdorf Randy J. Process device with vibration based diagnostics
US7627441B2 (en) 2003-09-30 2009-12-01 Rosemount Inc. Process device with vibration based diagnostics
US7523667B2 (en) 2003-12-23 2009-04-28 Rosemount Inc. Diagnostics of impulse piping in an industrial process
US20050132808A1 (en) * 2003-12-23 2005-06-23 Brown Gregory C. Diagnostics of impulse piping in an industrial process
US20070150305A1 (en) * 2004-02-18 2007-06-28 Klaus Abraham-Fuchs Method for selecting a potential participant for a medical study on the basis of a selection criterion
US20090043405A1 (en) * 2004-03-04 2009-02-12 Chester Daniel L Method and system of monitoring, sensor validation and predictive fault analysis
US20050210337A1 (en) * 2004-03-04 2005-09-22 Falconeer Technologies Llc Method and system of monitoring, sensor validation and predictive fault analysis
US7451003B2 (en) 2004-03-04 2008-11-11 Falconeer Technologies Llc Method and system of monitoring, sensor validation and predictive fault analysis
US6920799B1 (en) 2004-04-15 2005-07-26 Rosemount Inc. Magnetic flow meter with reference electrode
US7046180B2 (en) 2004-04-21 2006-05-16 Rosemount Inc. Analog-to-digital converter with range error detection
US10335906B2 (en) 2004-04-27 2019-07-02 Emerson Climate Technologies, Inc. Compressor diagnostic and protection system and method
US9669498B2 (en) 2004-04-27 2017-06-06 Emerson Climate Technologies, Inc. Compressor diagnostic and protection system and method
US9121407B2 (en) 2004-04-27 2015-09-01 Emerson Climate Technologies, Inc. Compressor diagnostic and protection system and method
US9086704B2 (en) 2004-08-11 2015-07-21 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US10558229B2 (en) 2004-08-11 2020-02-11 Emerson Climate Technologies Inc. Method and apparatus for monitoring refrigeration-cycle systems
US9690307B2 (en) 2004-08-11 2017-06-27 Emerson Climate Technologies, Inc. Method and apparatus for monitoring refrigeration-cycle systems
US9081394B2 (en) 2004-08-11 2015-07-14 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US9304521B2 (en) 2004-08-11 2016-04-05 Emerson Climate Technologies, Inc. Air filter monitoring system
US8974573B2 (en) 2004-08-11 2015-03-10 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US9046900B2 (en) 2004-08-11 2015-06-02 Emerson Climate Technologies, Inc. Method and apparatus for monitoring refrigeration-cycle systems
US9023136B2 (en) 2004-08-11 2015-05-05 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US9017461B2 (en) 2004-08-11 2015-04-28 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US9021819B2 (en) 2004-08-11 2015-05-05 Emerson Climate Technologies, Inc. Method and apparatus for monitoring a refrigeration-cycle system
US20060282580A1 (en) * 2005-06-08 2006-12-14 Russell Alden C Iii Multi-protocol field device interface with automatic bus detection
US8112565B2 (en) 2005-06-08 2012-02-07 Fisher-Rosemount Systems, Inc. Multi-protocol field device interface with automatic bus detection
US20070068225A1 (en) * 2005-09-29 2007-03-29 Brown Gregory C Leak detector for process valve
US7940189B2 (en) 2005-09-29 2011-05-10 Rosemount Inc. Leak detector for process valve
US20090303057A1 (en) * 2005-09-29 2009-12-10 Brown Gregory C Leak detector for process valve
US9885507B2 (en) 2006-07-19 2018-02-06 Emerson Climate Technologies, Inc. Protection and diagnostic module for a refrigeration system
US9823632B2 (en) 2006-09-07 2017-11-21 Emerson Climate Technologies, Inc. Compressor data module
US7953501B2 (en) 2006-09-25 2011-05-31 Fisher-Rosemount Systems, Inc. Industrial process control loop monitor
US8788070B2 (en) 2006-09-26 2014-07-22 Rosemount Inc. Automatic field device service adviser
US20080125884A1 (en) * 2006-09-26 2008-05-29 Schumacher Mark S Automatic field device service adviser
US7750642B2 (en) 2006-09-29 2010-07-06 Rosemount Inc. Magnetic flowmeter with verification
US7321846B1 (en) 2006-10-05 2008-01-22 Rosemount Inc. Two-wire process control loop diagnostics
US9310094B2 (en) 2007-07-30 2016-04-12 Emerson Climate Technologies, Inc. Portable method and apparatus for monitoring refrigerant-cycle systems
US10352602B2 (en) 2007-07-30 2019-07-16 Emerson Climate Technologies, Inc. Portable method and apparatus for monitoring refrigerant-cycle systems
US8898036B2 (en) 2007-08-06 2014-11-25 Rosemount Inc. Process variable transmitter with acceleration sensor
US20090043530A1 (en) * 2007-08-06 2009-02-12 Sittler Fred C Process variable transmitter with acceleration sensor
US20090083001A1 (en) * 2007-09-25 2009-03-26 Huisenga Garrie D Field device for digital process control loop diagnostics
US7590511B2 (en) 2007-09-25 2009-09-15 Rosemount Inc. Field device for digital process control loop diagnostics
US10458404B2 (en) 2007-11-02 2019-10-29 Emerson Climate Technologies, Inc. Compressor sensor module
US9194894B2 (en) 2007-11-02 2015-11-24 Emerson Climate Technologies, Inc. Compressor sensor module
US9140728B2 (en) 2007-11-02 2015-09-22 Emerson Climate Technologies, Inc. Compressor sensor module
US8112997B2 (en) 2008-04-28 2012-02-14 Siemens Energy, Inc. Condensate polisher circuit
US20090266076A1 (en) * 2008-04-28 2009-10-29 Siemens Energy, Inc. Condensate Polisher Circuit
US20090276136A1 (en) * 2008-04-30 2009-11-05 Steven Wayne Butler Method for calculating confidence on prediction in fault diagnosis systems
US8417432B2 (en) 2008-04-30 2013-04-09 United Technologies Corporation Method for calculating confidence on prediction in fault diagnosis systems
US20100017092A1 (en) * 2008-07-16 2010-01-21 Steven Wayne Butler Hybrid fault isolation system utilizing both model-based and empirical components
US20100199671A1 (en) * 2009-02-06 2010-08-12 Siemens Energy, Inc. Deaerator Apparatus in a Superatmospheric Condenser System
US8069667B2 (en) 2009-02-06 2011-12-06 Siemens Energy, Inc. Deaerator apparatus in a superatmospheric condenser system
US20100288054A1 (en) * 2009-05-12 2010-11-18 Foss Scot R System to detect poor process ground connections
US7921734B2 (en) 2009-05-12 2011-04-12 Rosemount Inc. System to detect poor process ground connections
FR2956701A1 (fr) * 2010-02-24 2011-08-26 Snecma Systeme de detection et de localisation de pannes et moteur comportant un tel systeme
WO2011104466A1 (fr) * 2010-02-24 2011-09-01 Snecma Systeme de detection et de localisation de pannes et moteur comportant un tel systeme
US8886582B2 (en) 2010-02-24 2014-11-11 Snecma Failure detection and locating system and engine containing the same
US9335042B2 (en) 2010-08-16 2016-05-10 Emerson Process Management Power & Water Solutions, Inc. Steam temperature control using dynamic matrix control
US9217565B2 (en) * 2010-08-16 2015-12-22 Emerson Process Management Power & Water Solutions, Inc. Dynamic matrix control of steam temperature with prevention of saturated steam entry into superheater
US9447963B2 (en) 2010-08-16 2016-09-20 Emerson Process Management Power & Water Solutions, Inc. Dynamic tuning of dynamic matrix control of steam temperature
US20120040299A1 (en) * 2010-08-16 2012-02-16 Emerson Process Management Power & Water Solutions, Inc. Dynamic matrix control of steam temperature with prevention of saturated steam entry into superheater
US9703287B2 (en) 2011-02-28 2017-07-11 Emerson Electric Co. Remote HVAC monitoring and diagnosis
US10884403B2 (en) 2011-02-28 2021-01-05 Emerson Electric Co. Remote HVAC monitoring and diagnosis
US9285802B2 (en) 2011-02-28 2016-03-15 Emerson Electric Co. Residential solutions HVAC monitoring and diagnosis
US10234854B2 (en) 2011-02-28 2019-03-19 Emerson Electric Co. Remote HVAC monitoring and diagnosis
US9207670B2 (en) 2011-03-21 2015-12-08 Rosemount Inc. Degrading sensor detection implemented within a transmitter
US9163828B2 (en) 2011-10-31 2015-10-20 Emerson Process Management Power & Water Solutions, Inc. Model-based load demand control
US10190766B2 (en) 2011-10-31 2019-01-29 Emerson Process Management Power & Water Solutions, Inc. Model-based load demand control
US8964338B2 (en) 2012-01-11 2015-02-24 Emerson Climate Technologies, Inc. System and method for compressor motor protection
US9590413B2 (en) 2012-01-11 2017-03-07 Emerson Climate Technologies, Inc. System and method for compressor motor protection
US9876346B2 (en) 2012-01-11 2018-01-23 Emerson Climate Technologies, Inc. System and method for compressor motor protection
US9052240B2 (en) 2012-06-29 2015-06-09 Rosemount Inc. Industrial process temperature transmitter with sensor stress diagnostics
US9762168B2 (en) 2012-09-25 2017-09-12 Emerson Climate Technologies, Inc. Compressor having a control and diagnostic module
US9310439B2 (en) 2012-09-25 2016-04-12 Emerson Climate Technologies, Inc. Compressor having a control and diagnostic module
US9207129B2 (en) 2012-09-27 2015-12-08 Rosemount Inc. Process variable transmitter with EMF detection and correction
US9602122B2 (en) 2012-09-28 2017-03-21 Rosemount Inc. Process variable measurement noise diagnostic
US9803902B2 (en) 2013-03-15 2017-10-31 Emerson Climate Technologies, Inc. System for refrigerant charge verification using two condenser coil temperatures
US9638436B2 (en) 2013-03-15 2017-05-02 Emerson Electric Co. HVAC system remote monitoring and diagnosis
US10775084B2 (en) 2013-03-15 2020-09-15 Emerson Climate Technologies, Inc. System for refrigerant charge verification
US10274945B2 (en) 2013-03-15 2019-04-30 Emerson Electric Co. HVAC system remote monitoring and diagnosis
US9551504B2 (en) 2013-03-15 2017-01-24 Emerson Electric Co. HVAC system remote monitoring and diagnosis
US10488090B2 (en) 2013-03-15 2019-11-26 Emerson Climate Technologies, Inc. System for refrigerant charge verification
US9765979B2 (en) 2013-04-05 2017-09-19 Emerson Climate Technologies, Inc. Heat-pump system with refrigerant charge diagnostics
US10443863B2 (en) 2013-04-05 2019-10-15 Emerson Climate Technologies, Inc. Method of monitoring charge condition of heat pump system
US10060636B2 (en) 2013-04-05 2018-08-28 Emerson Climate Technologies, Inc. Heat pump system with refrigerant charge diagnostics
US10357203B2 (en) * 2013-07-12 2019-07-23 Seiko Epson Corporation Biological information detection device
US20160113588A1 (en) * 2013-07-12 2016-04-28 Seiko Epson Corporation Biological information detection device
US9689823B2 (en) 2015-03-10 2017-06-27 Rosemount Inc. Steam quality meter and measurement method
CN108490238A (zh) * 2018-03-09 2018-09-04 中广核核电运营有限公司 汽轮发电机轴电流报警数据处理装置及方法
CN108490238B (zh) * 2018-03-09 2020-09-22 中广核核电运营有限公司 汽轮发电机轴电流报警数据处理装置及方法

Also Published As

Publication number Publication date
ES8700743A1 (es) 1986-10-16
MX157037A (es) 1988-10-18
AU572674B2 (en) 1988-05-12
EP0170516A2 (en) 1986-02-05
AU4514385A (en) 1986-02-06
IN164364B (ja) 1989-03-04
KR860000851A (ko) 1986-02-20
EP0170516B1 (en) 1991-04-24
JPS6155304A (ja) 1986-03-19
ES545729A0 (es) 1986-10-16
DE3582625D1 (de) 1991-05-29
EP0170516A3 (en) 1988-01-13
CA1237794A (en) 1988-06-07

Similar Documents

Publication Publication Date Title
US4644479A (en) Diagnostic apparatus
CA1231447A (en) Rule based diagnostic system with dynamic alteration capability
US5764509A (en) Industrial process surveillance system
US5070468A (en) Plant fault diagnosis system
US4755925A (en) Plant diagnostic system
EP0351833B1 (en) Plant fault diagnosis system
US4833622A (en) Intelligent chemistry management system
CN111664083A (zh) 一种基于贝叶斯网络的核电主泵故障诊断方法
US6484108B1 (en) Method for predicting recovery boiler leak detection system performance
Singer et al. A pattern-recognition-based, fault-tolerant monitoring and diagnostic technique
JP3188289B2 (ja) プラントの海水漏洩診断装置
JPH06288853A (ja) プロセス計装ラック
JPH05157668A (ja) プラント機器の異常検知方法及び装置
Kosebalaban et al. Integration of multivariate SPM and FDD by parity space technique for a food pasteurization process
JPH01269018A (ja) プラントの検出器の異常診断方法
JP2656264B2 (ja) 警報処理診断装置
JPH022404A (ja) プラント故障診断装置
JPS6227672A (ja) センサ−の自己診断装置
JPS624525B2 (ja)
JPS58205812A (ja) プラント診断装置
JPH04186119A (ja) 異常監視方法及び装置
JPH1021079A (ja) 半導体製造装置の故障診断装置及び半導体製造装置の故障診断方法
JPS6235635B2 (ja)
JPH04241028A (ja) エキスパートシステムにおける推論方法及び装置
JPH0413729B2 (ja)

Legal Events

Date Code Title Description
AS Assignment

Owner name: WESTINGHOUSE ELECTRIC CORPORATION WESTINGHOUSE BLD

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST.;ASSIGNORS:KEMPER, CHRISTIAN T.;BELLOWS, JAMES C.;KLEINOSKY, PAMELA J.;REEL/FRAME:004333/0150

Effective date: 19840724

CC Certificate of correction
AS Assignment

Owner name: HUGHES TOOL COMPANY

Free format text: CHANGE OF NAME;ASSIGNOR:HUGHES TOOL COMPANY-USA, A CORP. OF DE;REEL/FRAME:005169/0319

Effective date: 19881006

FPAY Fee payment

Year of fee payment: 4

FPAY Fee payment

Year of fee payment: 8

REMI Maintenance fee reminder mailed
LAPS Lapse for failure to pay maintenance fees
FP Lapsed due to failure to pay maintenance fee

Effective date: 19990217

STCH Information on status: patent discontinuation

Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362